Measuring the impact of novelty, bibliometric, and academic-network factors on citation count using a neural network

The ScholarNet and Artificial Intelligence (AI) Supervisor in Material Science Research

The past decade has witnessed extensive applications of artificial intelligence (AI) and robotics in chemistry and material science. However, the current focus mainly revolves around idea execution, neglecting the significance of idea generation, which plays a pivotal role in determining research novelty and potential breakthroughs. Concurrently, the exponential growth of scientific publications has resulted in overpublishing, making it challenging for researchers to grasp multiple fields effectively. As most opportunities for innovation lie in interdisciplinary realms, there is a risk of missing out on the development of new ideas. To address these challenges, we present a deep learning-based AI supervisor trained on correlation-based ScholarNet data of publications. Primarily tailored for material science, this AI supervisor excels in recommending research ideas, analyzing their novelty, and providing comprehensive guidance to researchers. By offering invaluable support in idea generation and novelty assessment, our AI supervisor has emerged as a promising digital infrastructure for future material science research.

Identify novel elements of knowledge with word embedding

As novelty is a core value in science, a reliable approach to measuring the novelty of scientific documents is critical. Previous novelty measures however had a few limitations. First, the majority of previous measures are based on recombinant novelty concept, attempting to identify a novel combination of knowledge elements, but insufficient effort has been made to identify a novel element itself (element novelty). Second, most previous measures are not validated, and it is unclear what aspect of newness is measured. Third, some of the previous measures can be computed only in certain scientific fields for technical constraints. This study thus aims to provide a validated and field-universal approach to computing element novelty. We drew on machine learning to develop a word embedding model, which allows us to extract semantic information from text data. Our validation analyses suggest that our word embedding model does convey semantic information. Based on the trained word embedding, we quantified the element novelty of a document by measuring its distance from the rest of the document universe. We then carried out a questionnaire survey to obtain self-reported novelty scores from 800 scientists. We found that our element novelty measure is significantly correlated with self-reported novelty in terms of discovering and identifying new phenomena, substances, molecules, etc. and that this correlation is observed across different scientific fields.

It Is Time for Anti-Bribery: Financial Institutions Set the New Strategic “Roadmap” to Mitigate Illicit Practices and Corruption in the Market

The financial sector is characterized by complexity due to the management of a large volume of transactions, which can lead to the difficulty of considering, identifying, and monitoring them. The lack of mechanisms in monitoring and control transactions can contribute to the development of illegal practices within a company, such as fraud, corruption, bribery, and money laundering. These phenomena can affect financial institutions negatively. Therefore, the development of an appropriate corporate governance system can ensure to members of the board and executives in a company that any illegal practice can be detected. This study aims to highlight the factors that contribute to the development of illegal practices within European financial institutions. This can help executives to plan and mitigate the illicit practices that may emerge. For this purpose, a binary logit regression analysis on panel data from 2018 to 2020 was applied to 336 European financial companies. The findings of this research emphasize the crucial role of corporate governance in the prevention of the development of illicit issues within European financial institutions, while human resources can be characterized as a pathway to corruption. Both factors, corporate governance and human resources, are main pillars of environmental, social, and corporate governance (ESG), which indicates the need of the financial sector in Europe for the elaboration of anti-corruption strategies. Thus, companies within the sector can improve their ESG score.

A modified LSTM network to predict the citation counts of papers

Quantifiable predictability in the citation counts of articles is significant in scientometrics and informetrics. Many metrics based on the citation counts can evaluate the scientific impact of research articles and journals. Utilising time series models, an article’s citation counts up to the yth year after publication can be predicted by those up to the previous years. However, the typically used models cannot predict the fat tail of the actual citation distributions. Thus, based on cumulative advantage of the citation behaviour, we propose a method to predict the accumulated citation counts, by using a random number sampled from a power-law distribution to modify the results given by a recurrent neural network (RNN), long short-term memory. Extensive experiments on the data set including 17 journals in information science verified the effectiveness of our method by the good fittings on distributions and evolutionary trends of the citation counts of articles. Our method has the potential to be extended to predict other popular assessment measures such as impact factor and h-index for journals.

Association Between Immediacy of Citations and Altmetrics in COVID-19 Research by Artificial Neural Networks

OBJECTIVES

Both citations and Altmetrics are indexes of influence of a publication, potentially useful, but to what extent that the professional-academic citation and media-dominated Altmetrics are consistent with each other is a topic worthy of being investigated. The objective is to show their correlation.

METHODS

DOI and citation information of coronavirus disease 2019 (COVID-19) researches were obtained from the Web of Science, its Altmetric indicators were collected from the Altmetrics. Correlation between the immediacy of citation and Altmetrics of COVID-19 research was studied by artificial neural networks.

RESULTS

Pearson coefficients are 0.962, 0.254, 0.222, 0.239, 0.363, 0.218, 0.136, 0.134, and 0.505 (P < 0.01) for dimensions citation, attention score, journal impact factor, news, blogs, Twitter, Facebook, video, and Mendeley correlated with the SCI citation, respectively. The citations from the Web of Science and that from the Altmetrics have deviance large enough in the current. Altmetric score is not precise to describe the immediacy of citations of academic publication in COVID-19 research.

CONCLUSIONS

The effects of news, blogs, Twitter, Facebook, video, and Mendeley on SCI citations are similar to that of the journal impact factor. This paper performs a pioneer study for investigating the role of academic topics across Altmetric sources on the dissemination of scholarly publications.